#!/bin/bash

#当前路径,不需要修改
cur_path=`pwd`/../

#集合通信参数,不需要修改

export RANK_SIZE=1
export JOB_ID=10087
RANK_ID_START=0


# 数据集路径,保持为空,不需要修改
data_path=''
#预训练模型地址
ckpt_path=''

#设置默认日志级别,不需要改
export ASCEND_GLOBAL_LOG_LEVEL=3
#export ASCEND_DEVICE_ID=4

#基础参数，需要模型审视修改
#网络名称，同目录名称
Network="stackgan_ID0760_for_TensorFlow"
#训练epoch
epochs=2
#训练batch_size
batch_size=64

#维测参数，precision_mode需要模型审视修改
precision_mode="allow_mix_precision"
#维持参数，以下不需要修改
over_dump=False
data_dump_flag=False
data_dump_step="10"
profiling=False

# 帮助信息，不需要修改
if [[ $1 == --help || $1 == -h ]];then
    echo"usage:./train_performance_1P.sh <args>"
    echo " "
    echo "parameter explain:
    --precision_mode         precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision)
    --over_dump                    if or not over detection, default is False
    --data_dump_flag                 data dump flag, default is False
    --data_dump_step                 data dump step, default is 10
    --profiling                    if or not profiling for performance debug, default is False
    --data_path                    source data of training
    --ckpt_path                         model
    -h/--help                        show help message
    "
    exit 1
fi

#参数校验，不需要修改
for para in $*
do
    if [[ $para == --precision_mode* ]];then
        precision_mode=`echo ${para#*=}`
    elif [[ $para == --over_dump* ]];then
        over_dump=`echo ${para#*=}`
        over_dump_path=${cur_path}/test/output/overflow_dump
        mkdir -p ${over_dump_path}
    elif [[ $para == --data_dump_flag* ]];then
        data_dump_flag=`echo ${para#*=}`
        data_dump_path=${cur_path}/test/output/data_dump
        mkdir -p ${data_dump_path}
    elif [[ $para == --data_dump_step* ]];then
        data_dump_step=`echo ${para#*=}`
    elif [[ $para == --profiling* ]];then
        profiling=`echo ${para#*=}`
        profiling_dump_path=${cur_path}/test/output/profiling
        mkdir -p ${profiling_dump_path}
    elif [[ $para == --data_path* ]];then
        data_path=`echo ${para#*=}`
    elif [[ $para == --ckpt_path* ]];then
        ckpt_path=`echo ${para#*=}`
        fi
done
#校验是否传入data_path,不需要修改
if [[$data_path == ""]];then
    echo "[Error] para \"data_path\" must be confing"
    exit 1
fi

#训练开始时间，不需要修改
start_time=$(date +%s)

#进入训练脚本目录，需要模型审视修改
cd $cur_path/
for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
do
   #设置环境变量，不需要修改
   echo "Device ID: $ASCEND_DEVICE_ID"
   export RANK_ID=$RANK_ID



   #创建DeviceID输出目录，不需要修改
   if [ -d ${cur_path}/test/output/${ASCEND_DEVICE_ID} ];then
      rm -rf ${cur_path}/test/output/${ASCEND_DEVICE_ID}
      mkdir -p ${cur_path}/test/output/$ASCEND_DEVICE_ID/ckpt
   else
      mkdir -p ${cur_path}/test/output/$ASCEND_DEVICE_ID/ckpt
   fi
   # 绑核，不需要的绑核的模型删除，需要的模型审视修改
   let a=RANK_ID*12
   let b=RANK_ID+1
   let c=b*12-1
   #执行训练脚本，以下传参不需要修改，其他需要模型审视修改
   #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path
   echo "----------------------start trainII----------------------"
   cp ${data_path}/Data/birds/npu_checkpoint/*  $cur_path/ckt_logs/birds/stageI
   floder="./ckt_logs/birds/stageI"
   files=$(ls $floder)
   for f in ${files}
   do
      echo "${f}"
   done
   sed -i "s@'Data/%s'@'${data_path}/Data/%s'@g" stageII/run_exp.py
   sed -i "/PRETRAINED_MODEL: 'model_164000.ckpt'/{s@'model_164000.ckpt'@'./ckt_logs/birds/stageI/model_164000.ckpt'@g}" stageII/cfg/birds.yml
   #sed -i "/MAX_EPOCH:/s@1200@1191@g" stageII/cfg/birds.yml  #全量
   sed -i "/SNAPSHOT_INTERVAL:/s@2000@500@g" stageII/cfg/birds.yml
   python3 stageII/run_exp.py \
      --cfg stageII/cfg/birds.yml > ${cur_path}/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1
   
   echo "----------------------start evaluate----------------------"
   sed -i "/FLAG: True/s@True@False@g" stageII/cfg/birds.yml
   sed -i "/PRETRAINED_MODEL:/{s@'./ckt_logs/birds/stageI/model_164000.ckpt'@'./ckt_logs/birds/stageII/model_165500.ckpt'@g}" stageII/cfg/birds.yml
   python3 stageII/run_exp.py \
      --cfg stageII/cfg/birds.yml > ${cur_path}/test/output/${ASCEND_DEVICE_ID}/train1_${ASCEND_DEVICE_ID}.log 2>&1

   echo "----------------------start sacore----------------------"
   cp -r ${data_path}/Data/birds/inception_finetuned_models $cur_path/
   cp -r ${data_path}/Data/birds/StackGAN-inception-model-master $cur_path/
   files=$(ls $cur_path)
   for f in ${files}
   do
      echo "${f}"
   done
   python3 StackGAN-inception-model-master/inception_score.py \
      --image_folder ./ckt_logs/birds/stageII/model_165500-1real-4samples/test/ > ${cur_path}/test/output/${ASCEND_DEVICE_ID}/test_${ASCEND_DEVICE_ID}.log 2>&1
done
wait
    #sed -i "/MAX_EPOCH:/s@1191@1200@g" stageII/cfg/birds.yml
    sed -i "/SNAPSHOT_INTERVAL:/s@500@2000@g" stageII/cfg/birds.yml
    sed -i "/FLAG: True/s@False@True@g" stageII/cfg/birds.yml
    sed -i "s@'${data_path}/Data/%s'@'Data/%s'@g" stageII/run_exp.py

#训练结束时间，不需要修改
end_time=$(date +%s)
e2e_time=$(( $end_time - $start_time ))

#结果打印，不需要修改
echo "------------------ Final result ------------------"
#输出性能FPS，需要模型审视修改
TrainingTime=`grep 'perf:' $cur_path/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|tail -3|head -n 1|awk '{print $4}'`
FPS=`grep 'fps:' $cur_path/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|tail -3|head -n 1|awk '{print $6}'`
#打印，不需要修改
echo "Final Performance TrainingTime : $TrainingTime"
echo "Final Performance images/sec : $FPS"

#输出训练精度,需要模型审视修改
train_accuracy=`grep  'mean:' $cur_path/test/output/${ASCEND_DEVICE_ID}/test_${ASCEND_DEVICE_ID}.log|awk '{print $2}'`

#打印，不需要修改
echo "Final Train Accuracy : ${train_accuracy}"
echo "E2E Training Duration sec : $e2e_time"

#性能看护结果汇总
#训练用例信息，不需要修改
BatchSize=${batch_size}
DeviceType=`uname -m`
CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'

##获取性能数据，不需要修改
#吞吐量
ActualFPS=${FPS}
#单迭代训练时长
#TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${FPS}'/69}'`

#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
grep 'd_loss' $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk  '{print $7}' >> $cur_path/test/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
#最后一个迭代loss值，不需要修改
ActualLoss=`awk 'END {print}' $cur_path/test/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`

#关键信息打印到${CaseName}.log中，不需修改
echo "Network = ${Network}" > $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "RankSize = ${RANK_SIZE}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "BatchSize = ${BatchSize}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "DeviceType = ${DeviceType}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CaseName = ${CaseName}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualFPS = ${ActualFPS}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainingTime = ${TrainingTime}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualLoss = ${ActualLoss}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log